Is it possible to reconstruct an accurate cell lineage using CRISPR recorders?

Salvador-Martínez, Irepan; Grillo, Marco; Averof, Michalis; Telford, Maximilian J.

doi:10.7554/elife.40292

Cited by 72 publications

(64 citation statements)

References 39 publications

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“…The high diversity of indel alleles here (9,936 unique indels across all cells 10 in mouse M5k; represented by the array of unique colors in the "character matrix", Fig. 2B) also reduces the probability of homoplasy, an issue that can complicate tree reconstruction and diminish tree accuracy (32,42).…”

Section: Reconstructed Cancer Cell Phylogeniesmentioning

confidence: 99%

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Quinn

Jones

Okimoto

et al. 2020

Preprint

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N.Y.).One Sentence Summary: Detailed single-cell phylogenies capture the frequency, tissue routes, and seeding patterns of metastasis in vivo. Abstract:25 2 Consequential events in cancer progression are typically rare and occur in the unobserved past. Detailed cell phylogenies can capture the history and chronology of such transient events -including metastasis. Here, we applied our Cas9-based lineage tracer to study metastatic progression in a lung cancer xenograft mouse model, revealing the underlying rates, routes, and patterns of metastasis. We report deeply resolved phylogenies for tens of thousands of metastatically disseminated cancer cells. We observe surprisingly diverse metastatic phenotypes, 5 ranging from metastasis-incompetent to highly aggressive populations, and these differences are associated with characteristic changes in transcriptional state, including differential expression of metastasis-related genes like IFI27 and ID3. We further show that metastases transit via tissue routes that are diverse, complex, and multidirectional, and identify examples of reseeding, seeding cascades, and parallel seeding topologies. More broadly, we demonstrate the power of next-generation lineage tracers to record cancer evolution at high resolution 10 and vast scale. Main Text:Cancer progression is governed by evolutionary principles (reviewed in (1)), which leave clear phylogenetic signatures upon every step of this process (2, 3), from early acquisition of oncogenic mutations (i.e., the relationships between normal and malignantly transformed cells (4)), to metastatic colonization of distant 15 tissues (i.e., the relationship between the primary tumor and metastases (5)), and finally adaptation to therapeutic challenges (i.e., the relationship between sensitive and resistant clones (6)). Metastasis is a particularly important step in cancer progression to study because it is chiefly responsible for disease relapse and mortality (7). Yet because metastatic events are intrinsically rare, transient, and stochastic (8,9), they are challenging to monitor in real time. Analogous to the cell fate maps that have played an essential role in deepening our understanding of 20 organismal development and cell type differentiation (10, 11), accurately reconstructed phylogenetic trees of tumors and metastases can reveal key features of this process, such as the clonality, timing, frequency, origins, and destinations of metastatic seeding (12).Lineage tracing techniques allow one to map the genealogy of related cells, providing a critical tool for exploring the phylogenetic principles of biological processes like cancer progression and metastasis. Classical

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Section: Reconstructed Cancer Cell Phylogeniesmentioning

confidence: 99%

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Quinn

Jones

Okimoto

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…The use of two or more arrays should therefore reconstruct colonies with greater depth and accuracy, as shown through simulations (Fig. S6) ( 27 , 46 ).…”

Section: Resultsmentioning

confidence: 99%

“…As a result, the use of trits dramatically improves the accuracy of multi-generation reconstruction and the multiplexibility of clonal classification in simulations, and allows the system to function across a broader range of edit rates compared to bit-based memory (Fig. 1B and C) ( 27 ).…”

Section: Resultsmentioning

confidence: 99%

“…Concatenating multiple trits in a compact array and integrating it at a safe harbor locus ( 35 – 37 ) increases the amount of memory while facilitating germline transmissibility ( 38 ). Edit strategies that rely on double-stranded breaks and endogenous DNA repair machinery are prone to information loss in arrays through inter-unit deletions ( 27 , 39 ). By contrast, integrases allow recombinational isolation between distinct trits in the same array.…”

Section: Resultsmentioning

confidence: 99%

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Imaging cell lineage with a synthetic digital recording system

Chow

Budde

Granados

et al. 2020

Preprint

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4030probability that two randomly selected cells with identical edit patterns are from the same clone. (D) Serine integrases enable trit designs compatible with FISH readout. Transcribed barcodes are flanked by two attPs and one attB site. Recombination results in either an inverted or deleted barcode, which can be distinguished by fluorescent probes (colored lines and asterisks) directed against either strand.

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“…The most common phylogenetic methods used to analyze GESTALT data are Camin-Sokal (C-S) parsimony [Camin and Sokal, 1965] and the neighbor-joining distance-based method [Saitou and Nei, 1987]. However these methods are blind to the operation of the GESTALT mutation process, so the accuracy of the estimated trees are poor [Salvador-Martínez et al, 2018]. In addition, existing methods supply branch length estimates in terms of an abstract notion of distance rather than time, limiting their interpretability.…”

Section: Introductionmentioning

confidence: 99%

Estimation of cell lineage trees by maximum-likelihood phylogenetics

Feng

DeWitt

McKenna

et al. 2019

Preprint

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9CRISPR technology has enabled large-scale cell lineage tracing for complex multicellular organisms 10 by mutating synthetic genomic barcodes during organismal development. However, these sophisti-11 cated biological tools currently use ad-hoc and outmoded computational methods to reconstruct 12 the cell lineage tree from the mutated barcodes. Because these methods are agnostic to the 13 biological mechanism, they are unable to take full advantage of the data's structure. We propose a 14 statistical model for the mutation process and develop a procedure to estimate the tree topology, 15 branch lengths, and mutation parameters by iteratively applying penalized maximum likelihood 16 estimation. In contrast to existing techniques, our method estimates time along each branch, rather 17 than number of mutation events, thus providing a detailed account of tissue-type differentiation. 18 Via simulations, we demonstrate that our method is substantially more accurate than existing ap-19 proaches. Our reconstructed trees also better recapitulate known aspects of zebrafish development 20 and reproduce similar results across fish replicates. 21 42 1 Genome Editing of Synthetic Target Arrays for Lineage Tracing 1 of 38Manuscript submitted to eLife parsimony is unable to distinguish between equally parsimonious trees, so obtaining a single tree 43 estimate is difficult in practice: We find over ten thousand parsimony-optimal trees for existing 44 datasets. To address these challenges, we set out to develop a statistical model of the mutation 45 process, allowing us to estimate branch lengths that correspond to time as well as the mutation 46 parameters. 47 157 estimating the tree topology using C-S parsimony [Camin and Sokal, 1965] or neighbor-joining 158 (NJ) [Saitou and Nei, 1987] and then applying semiparametric rate smoothing (chronos in the R 159 4 of 38

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Is it possible to reconstruct an accurate cell lineage using CRISPR recorders?

Cited by 72 publications

References 39 publications

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Single-cell lineages reveal the rates, routes, and drivers of metastasis in cancer xenografts

Imaging cell lineage with a synthetic digital recording system

Estimation of cell lineage trees by maximum-likelihood phylogenetics

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